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The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Modelling dyadic interaction with Hawkes processes.

Peter F Halpin1, Paul De Boeck

  • 1Department of Humanities and Social Science in the Professions, New York University, 246 Greene St, Office 316E, 10013-6677, New York, USA, peter.halpin@nyu.edu.

Psychometrika
|October 5, 2013
PubMed
Summary
This summary is machine-generated.

We used the Hawkes process to analyze how one person's actions influence another's in dyadic interactions. This method models how past events increase the likelihood of future actions, demonstrated with workplace email data.

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06:41

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Published on: February 25, 2011

Area of Science:

  • Computational Social Science
  • Statistical Modeling
  • Network Analysis

Background:

  • Dyadic interactions are common in various systems, from social networks to biological systems.
  • Understanding the temporal dynamics and influence within these interactions is crucial.
  • Traditional models may not fully capture the self-exciting nature of these interactions.

Purpose of the Study:

  • To introduce and apply the Hawkes process for analyzing dyadic interactions.
  • To explore two representations of the Hawkes process: conditional intensity and cluster Poisson process.
  • To demonstrate the practical application of the Hawkes process using real-world data.

Main Methods:

  • Modeling dyadic interactions using the Hawkes process, a self-exciting point process.
  • Representing the Hawkes process via conditional intensity functions for continuous-time analysis.
  • Utilizing the cluster Poisson process representation for maximum likelihood estimation via the Expectation-Maximization (EM) algorithm.
  • Applying the model to analyze email transaction data in a workplace setting.

Main Results:

  • The Hawkes process effectively models the excitatory nature of dyadic interactions, where actions increase future action probabilities.
  • Both conditional intensity and cluster Poisson process representations offer valuable insights into interaction dynamics.
  • The EM algorithm provides an efficient method for parameter estimation in the cluster Poisson process framework.
  • Analysis of email data revealed patterns of influence and response within workplace communication.

Conclusions:

  • The Hawkes process is a powerful tool for quantifying influence and temporal dependencies in dyadic interactions.
  • The chosen representations and estimation methods are suitable for analyzing complex interaction data.
  • This approach offers a robust framework for understanding and predicting behavior in systems characterized by mutual influence.